Overview

Brought to you by YData

Dataset statistics

Number of variables28
Number of observations1503
Missing cells1316
Missing cells (%)3.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory328.9 KiB
Average record size in memory224.1 B

Variable types

Text14
Categorical5
Numeric9

Alerts

Academic_Reputation_Score is highly overall correlated with Employer_Reputation_Score and 3 other fieldsHigh correlation
Citations_per_Faculty_Score is highly overall correlated with International_Research_Network_Score and 1 other fieldsHigh correlation
Employer_Reputation_Score is highly overall correlated with Academic_Reputation_Score and 2 other fieldsHigh correlation
Employment_Outcomes_Score is highly overall correlated with Academic_Reputation_Score and 2 other fieldsHigh correlation
International_Faculty_Score is highly overall correlated with International_Students_ScoreHigh correlation
International_Research_Network_Score is highly overall correlated with Academic_Reputation_Score and 2 other fieldsHigh correlation
International_Students_Score is highly overall correlated with International_Faculty_ScoreHigh correlation
Sustainability_Score is highly overall correlated with Academic_Reputation_Score and 4 other fieldsHigh correlation
RANK_2024 has 21 (1.4%) missing valuesMissing
STATUS has 37 (2.5%) missing valuesMissing
International_Faculty_Score has 100 (6.7%) missing valuesMissing
International_Faculty_Rank has 100 (6.7%) missing valuesMissing
International_Students_Score has 58 (3.9%) missing valuesMissing
International_Students_Rank has 58 (3.9%) missing valuesMissing
Sustainability_Score has 19 (1.3%) missing valuesMissing
Sustainability_Rank has 19 (1.3%) missing valuesMissing
Overall_Score has 902 (60.0%) missing valuesMissing
Institution_Name has unique valuesUnique

Reproduction

Analysis started2025-10-23 05:44:33.258827
Analysis finished2025-10-23 05:44:43.947473
Duration10.69 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct388
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
2025-10-23T11:14:44.211232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.7578177
Min length1

Characters and Unicode

Total characters8654
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique214 ?
Unique (%)14.2%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row5
ValueCountFrequency (%)
1201-1400199
 
13.2%
1001-1200197
 
13.1%
1401102
 
6.8%
951-100053
 
3.5%
901-95051
 
3.4%
851-90050
 
3.3%
801-85049
 
3.3%
721-73014
 
0.9%
641-65013
 
0.9%
741-75013
 
0.9%
Other values (378)762
50.7%
2025-10-23T11:14:44.601484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
02292
26.5%
12115
24.4%
-801
 
9.3%
2664
 
7.7%
4555
 
6.4%
5477
 
5.5%
6364
 
4.2%
9360
 
4.2%
7357
 
4.1%
8316
 
3.7%
Other values (2)353
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)8654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02292
26.5%
12115
24.4%
-801
 
9.3%
2664
 
7.7%
4555
 
6.4%
5477
 
5.5%
6364
 
4.2%
9360
 
4.2%
7357
 
4.1%
8316
 
3.7%
Other values (2)353
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02292
26.5%
12115
24.4%
-801
 
9.3%
2664
 
7.7%
4555
 
6.4%
5477
 
5.5%
6364
 
4.2%
9360
 
4.2%
7357
 
4.1%
8316
 
3.7%
Other values (2)353
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02292
26.5%
12115
24.4%
-801
 
9.3%
2664
 
7.7%
4555
 
6.4%
5477
 
5.5%
6364
 
4.2%
9360
 
4.2%
7357
 
4.1%
8316
 
3.7%
Other values (2)353
 
4.1%

RANK_2024
Text

Missing 

Distinct378
Distinct (%)25.5%
Missing21
Missing (%)1.4%
Memory size11.9 KiB
2025-10-23T11:14:44.910789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.7881242
Min length1

Characters and Unicode

Total characters8578
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique202 ?
Unique (%)13.6%

Sample

1st row1
2nd row6
3rd row3
4th row4
5th row2
ValueCountFrequency (%)
1201-1400206
 
13.9%
1001-1200199
 
13.4%
140178
 
5.3%
951-100054
 
3.6%
801-85052
 
3.5%
851-90049
 
3.3%
901-95047
 
3.2%
641-65014
 
0.9%
661-67014
 
0.9%
721-73012
 
0.8%
Other values (368)757
51.1%
2025-10-23T11:14:45.325259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
02298
26.8%
12099
24.5%
-805
 
9.4%
2658
 
7.7%
4561
 
6.5%
5466
 
5.4%
6362
 
4.2%
9353
 
4.1%
7337
 
3.9%
8311
 
3.6%
Other values (2)328
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)8578
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02298
26.8%
12099
24.5%
-805
 
9.4%
2658
 
7.7%
4561
 
6.5%
5466
 
5.4%
6362
 
4.2%
9353
 
4.1%
7337
 
3.9%
8311
 
3.6%
Other values (2)328
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8578
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02298
26.8%
12099
24.5%
-805
 
9.4%
2658
 
7.7%
4561
 
6.5%
5466
 
5.4%
6362
 
4.2%
9353
 
4.1%
7337
 
3.9%
8311
 
3.6%
Other values (2)328
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8578
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02298
26.8%
12099
24.5%
-805
 
9.4%
2658
 
7.7%
4561
 
6.5%
5466
 
5.4%
6362
 
4.2%
9353
 
4.1%
7337
 
3.9%
8311
 
3.6%
Other values (2)328
 
3.8%

Institution_Name
Text

Unique 

Distinct1503
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
2025-10-23T11:14:45.620423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length117
Median length70
Mean length29.30672
Min length3

Characters and Unicode

Total characters44048
Distinct characters96
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1503 ?
Unique (%)100.0%

Sample

1st rowMassachusetts Institute of Technology (MIT)
2nd rowImperial College London
3rd rowUniversity of Oxford
4th rowHarvard University
5th rowUniversity of Cambridge
ValueCountFrequency (%)
university1084
 
19.1%
of536
 
9.4%
de186
 
3.3%
universidad159
 
2.8%
technology115
 
2.0%
national68
 
1.2%
and68
 
1.2%
state62
 
1.1%
the56
 
1.0%
institute55
 
1.0%
Other values (1948)3297
58.0%
2025-10-23T11:14:46.029110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i4563
 
10.4%
4292
 
9.7%
e3493
 
7.9%
n3288
 
7.5%
a2819
 
6.4%
r2524
 
5.7%
t2462
 
5.6%
s2300
 
5.2%
o2082
 
4.7%
U1607
 
3.6%
Other values (86)14618
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)44048
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i4563
 
10.4%
4292
 
9.7%
e3493
 
7.9%
n3288
 
7.5%
a2819
 
6.4%
r2524
 
5.7%
t2462
 
5.6%
s2300
 
5.2%
o2082
 
4.7%
U1607
 
3.6%
Other values (86)14618
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44048
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i4563
 
10.4%
4292
 
9.7%
e3493
 
7.9%
n3288
 
7.5%
a2819
 
6.4%
r2524
 
5.7%
t2462
 
5.6%
s2300
 
5.2%
o2082
 
4.7%
U1607
 
3.6%
Other values (86)14618
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44048
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i4563
 
10.4%
4292
 
9.7%
e3493
 
7.9%
n3288
 
7.5%
a2819
 
6.4%
r2524
 
5.7%
t2462
 
5.6%
s2300
 
5.2%
o2082
 
4.7%
U1607
 
3.6%
Other values (86)14618
33.2%
Distinct106
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
2025-10-23T11:14:46.206368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length31
Median length20
Mean length9.1210912
Min length4

Characters and Unicode

Total characters13709
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)1.1%

Sample

1st rowUnited States
2nd rowUnited Kingdom
3rd rowUnited Kingdom
4th rowUnited States
5th rowUnited Kingdom
ValueCountFrequency (%)
united299
 
14.6%
states197
 
9.6%
kingdom90
 
4.4%
china71
 
3.5%
mainland71
 
3.5%
south54
 
2.6%
japan49
 
2.4%
germany48
 
2.3%
russia47
 
2.3%
india46
 
2.2%
Other values (114)1075
52.5%
2025-10-23T11:14:46.478210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a1839
13.4%
n1290
 
9.4%
i1226
 
8.9%
e1046
 
7.6%
t999
 
7.3%
d717
 
5.2%
544
 
4.0%
s512
 
3.7%
r504
 
3.7%
l454
 
3.3%
Other values (43)4578
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)13709
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1839
13.4%
n1290
 
9.4%
i1226
 
8.9%
e1046
 
7.6%
t999
 
7.3%
d717
 
5.2%
544
 
4.0%
s512
 
3.7%
r504
 
3.7%
l454
 
3.3%
Other values (43)4578
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13709
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1839
13.4%
n1290
 
9.4%
i1226
 
8.9%
e1046
 
7.6%
t999
 
7.3%
d717
 
5.2%
544
 
4.0%
s512
 
3.7%
r504
 
3.7%
l454
 
3.3%
Other values (43)4578
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13709
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1839
13.4%
n1290
 
9.4%
i1226
 
8.9%
e1046
 
7.6%
t999
 
7.3%
d717
 
5.2%
544
 
4.0%
s512
 
3.7%
r504
 
3.7%
l454
 
3.3%
Other values (43)4578
33.4%

Region
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
Asia
501 
Europe
497 
Americas
418 
Oceania
 
46
Africa
 
40

Length

Max length14
Median length8
Mean length5.9254824
Min length4

Characters and Unicode

Total characters8906
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAmericas
2nd rowEurope
3rd rowEurope
4th rowAmericas
5th rowEurope

Common Values

ValueCountFrequency (%)
Asia501
33.3%
Europe497
33.1%
Americas418
27.8%
Oceania46
 
3.1%
Africa40
 
2.7%
Not Classified1
 
0.1%

Length

2025-10-23T11:14:46.560207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T11:14:46.629941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
asia501
33.3%
europe497
33.0%
americas418
27.8%
oceania46
 
3.1%
africa40
 
2.7%
not1
 
0.1%
classified1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a1052
11.8%
i1007
11.3%
e962
10.8%
A959
10.8%
r955
10.7%
s921
10.3%
c504
5.7%
o498
5.6%
E497
5.6%
p497
5.6%
Other values (11)1054
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)8906
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1052
11.8%
i1007
11.3%
e962
10.8%
A959
10.8%
r955
10.7%
s921
10.3%
c504
5.7%
o498
5.6%
E497
5.6%
p497
5.6%
Other values (11)1054
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8906
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1052
11.8%
i1007
11.3%
e962
10.8%
A959
10.8%
r955
10.7%
s921
10.3%
c504
5.7%
o498
5.6%
E497
5.6%
p497
5.6%
Other values (11)1054
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8906
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1052
11.8%
i1007
11.3%
e962
10.8%
A959
10.8%
r955
10.7%
s921
10.3%
c504
5.7%
o498
5.6%
E497
5.6%
p497
5.6%
Other values (11)1054
11.8%

SIZE
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
L
691 
M
372 
XL
352 
S
88 

Length

Max length2
Median length1
Mean length1.2341983
Min length1

Characters and Unicode

Total characters1855
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowL
3rd rowL
4th rowL
5th rowL

Common Values

ValueCountFrequency (%)
L691
46.0%
M372
24.8%
XL352
23.4%
S88
 
5.9%

Length

2025-10-23T11:14:46.746137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T11:14:46.811119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
l691
46.0%
m372
24.8%
xl352
23.4%
s88
 
5.9%

Most occurring characters

ValueCountFrequency (%)
L1043
56.2%
M372
 
20.1%
X352
 
19.0%
S88
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1855
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L1043
56.2%
M372
 
20.1%
X352
 
19.0%
S88
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1855
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L1043
56.2%
M372
 
20.1%
X352
 
19.0%
S88
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1855
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L1043
56.2%
M372
 
20.1%
X352
 
19.0%
S88
 
4.7%

FOCUS
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
FC
592 
CO
480 
FO
353 
SP
78 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3006
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCO
2nd rowFC
3rd rowFC
4th rowFC
5th rowFC

Common Values

ValueCountFrequency (%)
FC592
39.4%
CO480
31.9%
FO353
23.5%
SP78
 
5.2%

Length

2025-10-23T11:14:46.883049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T11:14:46.939894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fc592
39.4%
co480
31.9%
fo353
23.5%
sp78
 
5.2%

Most occurring characters

ValueCountFrequency (%)
C1072
35.7%
F945
31.4%
O833
27.7%
S78
 
2.6%
P78
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)3006
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C1072
35.7%
F945
31.4%
O833
27.7%
S78
 
2.6%
P78
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3006
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C1072
35.7%
F945
31.4%
O833
27.7%
S78
 
2.6%
P78
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3006
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C1072
35.7%
F945
31.4%
O833
27.7%
S78
 
2.6%
P78
 
2.6%

RES.
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
VH
1021 
HI
362 
MD
104 
LO
 
16

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3006
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVH
2nd rowVH
3rd rowVH
4th rowVH
5th rowVH

Common Values

ValueCountFrequency (%)
VH1021
67.9%
HI362
 
24.1%
MD104
 
6.9%
LO16
 
1.1%

Length

2025-10-23T11:14:47.010666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T11:14:47.066283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
vh1021
67.9%
hi362
 
24.1%
md104
 
6.9%
lo16
 
1.1%

Most occurring characters

ValueCountFrequency (%)
H1383
46.0%
V1021
34.0%
I362
 
12.0%
M104
 
3.5%
D104
 
3.5%
L16
 
0.5%
O16
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)3006
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
H1383
46.0%
V1021
34.0%
I362
 
12.0%
M104
 
3.5%
D104
 
3.5%
L16
 
0.5%
O16
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3006
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
H1383
46.0%
V1021
34.0%
I362
 
12.0%
M104
 
3.5%
D104
 
3.5%
L16
 
0.5%
O16
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3006
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
H1383
46.0%
V1021
34.0%
I362
 
12.0%
M104
 
3.5%
D104
 
3.5%
L16
 
0.5%
O16
 
0.5%

STATUS
Categorical

Missing 

Distinct3
Distinct (%)0.2%
Missing37
Missing (%)2.5%
Memory size11.9 KiB
A
1165 
B
249 
C
 
52

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1466
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowA
3rd rowA
4th rowB
5th rowA

Common Values

ValueCountFrequency (%)
A1165
77.5%
B249
 
16.6%
C52
 
3.5%
(Missing)37
 
2.5%

Length

2025-10-23T11:14:47.141801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T11:14:47.193496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a1165
79.5%
b249
 
17.0%
c52
 
3.5%

Most occurring characters

ValueCountFrequency (%)
A1165
79.5%
B249
 
17.0%
C52
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1466
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A1165
79.5%
B249
 
17.0%
C52
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1466
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A1165
79.5%
B249
 
17.0%
C52
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1466
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A1165
79.5%
B249
 
17.0%
C52
 
3.5%

Academic_Reputation_Score
Real number (ℝ)

High correlation 

Distinct472
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.286693
Minimum1.3
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.9 KiB
2025-10-23T11:14:47.261636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile3.8
Q16.3
median11
Q323.5
95-th percentile76.87
Maximum100
Range98.7
Interquartile range (IQR)17.2

Descriptive statistics

Standard deviation22.326168
Coefficient of variation (CV)1.1005326
Kurtosis3.52393
Mean20.286693
Median Absolute Deviation (MAD)6
Skewness2.0240885
Sum30490.9
Variance498.45776
MonotonicityNot monotonic
2025-10-23T11:14:47.343311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.520
 
1.3%
4.419
 
1.3%
6.118
 
1.2%
5.816
 
1.1%
4.916
 
1.1%
5.216
 
1.1%
6.615
 
1.0%
4.115
 
1.0%
6.915
 
1.0%
6.315
 
1.0%
Other values (462)1338
89.0%
ValueCountFrequency (%)
1.31
 
0.1%
1.61
 
0.1%
2.22
 
0.1%
2.64
0.3%
2.73
0.2%
2.81
 
0.1%
2.91
 
0.1%
34
0.3%
3.15
0.3%
3.26
0.4%
ValueCountFrequency (%)
1008
0.5%
99.91
 
0.1%
99.81
 
0.1%
99.71
 
0.1%
99.61
 
0.1%
99.53
 
0.2%
99.21
 
0.1%
99.11
 
0.1%
98.82
 
0.1%
98.53
 
0.2%
Distinct601
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
2025-10-23T11:14:47.577799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.5289421
Min length1

Characters and Unicode

Total characters5304
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique600 ?
Unique (%)39.9%

Sample

1st row4
2nd row22
3rd row2
4th row1
5th row3
ValueCountFrequency (%)
601903
60.1%
291
 
0.1%
5981
 
0.1%
41
 
0.1%
221
 
0.1%
21
 
0.1%
11
 
0.1%
31
 
0.1%
431
 
0.1%
5171
 
0.1%
Other values (591)591
39.3%
2025-10-23T11:14:47.866719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11123
21.2%
61024
19.3%
01014
19.1%
+903
17.0%
3220
 
4.1%
5220
 
4.1%
4220
 
4.1%
2220
 
4.1%
7120
 
2.3%
9120
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)5304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11123
21.2%
61024
19.3%
01014
19.1%
+903
17.0%
3220
 
4.1%
5220
 
4.1%
4220
 
4.1%
2220
 
4.1%
7120
 
2.3%
9120
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11123
21.2%
61024
19.3%
01014
19.1%
+903
17.0%
3220
 
4.1%
5220
 
4.1%
4220
 
4.1%
2220
 
4.1%
7120
 
2.3%
9120
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11123
21.2%
61024
19.3%
01014
19.1%
+903
17.0%
3220
 
4.1%
5220
 
4.1%
4220
 
4.1%
2220
 
4.1%
7120
 
2.3%
9120
 
2.3%

Employer_Reputation_Score
Real number (ℝ)

High correlation 

Distinct500
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.787292
Minimum1.1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.9 KiB
2025-10-23T11:14:47.946796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile2
Q14.3
median9.4
Q325.3
95-th percentile79.96
Maximum100
Range98.9
Interquartile range (IQR)21

Descriptive statistics

Standard deviation23.784738
Coefficient of variation (CV)1.2020209
Kurtosis2.6872742
Mean19.787292
Median Absolute Deviation (MAD)6.5
Skewness1.8481668
Sum29740.3
Variance565.71377
MonotonicityNot monotonic
2025-10-23T11:14:48.027460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.524
 
1.6%
2.418
 
1.2%
2.318
 
1.2%
2.717
 
1.1%
2.616
 
1.1%
315
 
1.0%
1.815
 
1.0%
5.315
 
1.0%
2.815
 
1.0%
3.914
 
0.9%
Other values (490)1336
88.9%
ValueCountFrequency (%)
1.11
 
0.1%
1.23
 
0.2%
1.35
 
0.3%
1.47
0.5%
1.54
 
0.3%
1.612
0.8%
1.710
0.7%
1.815
1.0%
1.911
0.7%
211
0.7%
ValueCountFrequency (%)
1006
0.4%
99.91
 
0.1%
99.82
 
0.1%
99.61
 
0.1%
99.51
 
0.1%
99.11
 
0.1%
991
 
0.1%
98.82
 
0.1%
98.62
 
0.1%
98.32
 
0.1%
Distinct601
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
2025-10-23T11:14:48.269925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.5289421
Min length1

Characters and Unicode

Total characters5304
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique600 ?
Unique (%)39.9%

Sample

1st row2
2nd row11
3rd row5
4th row1
5th row4
ValueCountFrequency (%)
601903
60.1%
331
 
0.1%
4151
 
0.1%
21
 
0.1%
111
 
0.1%
51
 
0.1%
11
 
0.1%
41
 
0.1%
501
 
0.1%
5351
 
0.1%
Other values (591)591
39.3%
2025-10-23T11:14:48.554221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11123
21.2%
61024
19.3%
01014
19.1%
+903
17.0%
2220
 
4.1%
3220
 
4.1%
4220
 
4.1%
5220
 
4.1%
9120
 
2.3%
7120
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)5304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11123
21.2%
61024
19.3%
01014
19.1%
+903
17.0%
2220
 
4.1%
3220
 
4.1%
4220
 
4.1%
5220
 
4.1%
9120
 
2.3%
7120
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11123
21.2%
61024
19.3%
01014
19.1%
+903
17.0%
2220
 
4.1%
3220
 
4.1%
4220
 
4.1%
5220
 
4.1%
9120
 
2.3%
7120
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11123
21.2%
61024
19.3%
01014
19.1%
+903
17.0%
2220
 
4.1%
3220
 
4.1%
4220
 
4.1%
5220
 
4.1%
9120
 
2.3%
7120
 
2.3%

Faculty_Student_Score
Real number (ℝ)

Distinct590
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.128676
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.9 KiB
2025-10-23T11:14:48.626110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.01
Q17.3
median16
Q340.4
95-th percentile92.2
Maximum100
Range99
Interquartile range (IQR)33.1

Descriptive statistics

Standard deviation27.613017
Coefficient of variation (CV)0.98166785
Kurtosis0.39030144
Mean28.128676
Median Absolute Deviation (MAD)11.4
Skewness1.22711
Sum42277.4
Variance762.4787
MonotonicityNot monotonic
2025-10-23T11:14:48.718004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10017
 
1.1%
4.613
 
0.9%
5.213
 
0.9%
3.313
 
0.9%
3.512
 
0.8%
5.712
 
0.8%
4.111
 
0.7%
4.510
 
0.7%
4.410
 
0.7%
3.910
 
0.7%
Other values (580)1382
91.9%
ValueCountFrequency (%)
11
 
0.1%
1.11
 
0.1%
1.22
0.1%
1.32
0.1%
1.42
0.1%
1.53
0.2%
1.62
0.1%
1.72
0.1%
1.84
0.3%
1.94
0.3%
ValueCountFrequency (%)
10017
1.1%
99.96
 
0.4%
99.84
 
0.3%
99.73
 
0.2%
99.41
 
0.1%
99.31
 
0.1%
99.12
 
0.1%
993
 
0.2%
98.92
 
0.1%
98.81
 
0.1%
Distinct683
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
2025-10-23T11:14:48.970732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.4757152
Min length1

Characters and Unicode

Total characters5224
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique682 ?
Unique (%)45.4%

Sample

1st row11
2nd row45
3rd row8
4th row53
5th row17
ValueCountFrequency (%)
701821
54.6%
6991
 
0.1%
111
 
0.1%
451
 
0.1%
81
 
0.1%
531
 
0.1%
171
 
0.1%
121
 
0.1%
1991
 
0.1%
1891
 
0.1%
Other values (673)673
44.8%
2025-10-23T11:14:49.259420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11053
20.2%
7958
18.3%
0950
18.2%
+821
15.7%
4237
 
4.5%
3235
 
4.5%
2234
 
4.5%
5233
 
4.5%
6230
 
4.4%
9137
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)5224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11053
20.2%
7958
18.3%
0950
18.2%
+821
15.7%
4237
 
4.5%
3235
 
4.5%
2234
 
4.5%
5233
 
4.5%
6230
 
4.4%
9137
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11053
20.2%
7958
18.3%
0950
18.2%
+821
15.7%
4237
 
4.5%
3235
 
4.5%
2234
 
4.5%
5233
 
4.5%
6230
 
4.4%
9137
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11053
20.2%
7958
18.3%
0950
18.2%
+821
15.7%
4237
 
4.5%
3235
 
4.5%
2234
 
4.5%
5233
 
4.5%
6230
 
4.4%
9137
 
2.6%

Citations_per_Faculty_Score
Real number (ℝ)

High correlation 

Distinct566
Distinct (%)37.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.50346
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.9 KiB
2025-10-23T11:14:49.333919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.21
Q12.8
median9.9
Q336.4
95-th percentile90.13
Maximum100
Range99
Interquartile range (IQR)33.6

Descriptive statistics

Standard deviation27.870692
Coefficient of variation (CV)1.1858123
Kurtosis0.71074782
Mean23.50346
Median Absolute Deviation (MAD)8.3
Skewness1.3498671
Sum35325.7
Variance776.77545
MonotonicityNot monotonic
2025-10-23T11:14:49.416852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.131
 
2.1%
1.331
 
2.1%
1.429
 
1.9%
1.228
 
1.9%
1.627
 
1.8%
1.527
 
1.8%
1.920
 
1.3%
1.720
 
1.3%
2.120
 
1.3%
1.819
 
1.3%
Other values (556)1251
83.2%
ValueCountFrequency (%)
117
1.1%
1.131
2.1%
1.228
1.9%
1.331
2.1%
1.429
1.9%
1.527
1.8%
1.627
1.8%
1.720
1.3%
1.819
1.3%
1.920
1.3%
ValueCountFrequency (%)
1009
0.6%
99.94
0.3%
99.81
 
0.1%
99.72
 
0.1%
99.61
 
0.1%
99.52
 
0.1%
99.42
 
0.1%
99.31
 
0.1%
99.14
0.3%
991
 
0.1%
Distinct699
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
2025-10-23T11:14:49.664038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.4624085
Min length1

Characters and Unicode

Total characters5204
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique696 ?
Unique (%)46.3%

Sample

1st row9
2nd row54
3rd row93
4th row1
5th row96
ValueCountFrequency (%)
701803
53.4%
5342
 
0.1%
3362
 
0.1%
541
 
0.1%
931
 
0.1%
11
 
0.1%
961
 
0.1%
271
 
0.1%
351
 
0.1%
611
 
0.1%
Other values (689)689
45.8%
2025-10-23T11:14:49.954895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11043
20.0%
7942
18.1%
0934
17.9%
+803
15.4%
3241
 
4.6%
6241
 
4.6%
5240
 
4.6%
4240
 
4.6%
2240
 
4.6%
8140
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)5204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11043
20.0%
7942
18.1%
0934
17.9%
+803
15.4%
3241
 
4.6%
6241
 
4.6%
5240
 
4.6%
4240
 
4.6%
2240
 
4.6%
8140
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11043
20.0%
7942
18.1%
0934
17.9%
+803
15.4%
3241
 
4.6%
6241
 
4.6%
5240
 
4.6%
4240
 
4.6%
2240
 
4.6%
8140
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11043
20.0%
7942
18.1%
0934
17.9%
+803
15.4%
3241
 
4.6%
6241
 
4.6%
5240
 
4.6%
4240
 
4.6%
2240
 
4.6%
8140
 
2.7%

International_Faculty_Score
Real number (ℝ)

High correlation  Missing 

Distinct531
Distinct (%)37.8%
Missing100
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean30.736707
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.9 KiB
2025-10-23T11:14:50.028413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q14.2
median12.5
Q351.6
95-th percentile100
Maximum100
Range99
Interquartile range (IQR)47.4

Descriptive statistics

Standard deviation34.344365
Coefficient of variation (CV)1.117373
Kurtosis-0.52621926
Mean30.736707
Median Absolute Deviation (MAD)10.3
Skewness1.0142597
Sum43123.6
Variance1179.5354
MonotonicityNot monotonic
2025-10-23T11:14:50.112602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10080
 
5.3%
1.418
 
1.2%
2.217
 
1.1%
1.317
 
1.1%
2.615
 
1.0%
2.914
 
0.9%
1.114
 
0.9%
1.713
 
0.9%
3.813
 
0.9%
3.213
 
0.9%
Other values (521)1189
79.1%
(Missing)100
 
6.7%
ValueCountFrequency (%)
16
 
0.4%
1.114
0.9%
1.27
 
0.5%
1.317
1.1%
1.418
1.2%
1.512
0.8%
1.69
0.6%
1.713
0.9%
1.813
0.9%
1.911
0.7%
ValueCountFrequency (%)
10080
5.3%
99.96
 
0.4%
99.85
 
0.3%
99.61
 
0.1%
99.51
 
0.1%
99.43
 
0.2%
99.32
 
0.1%
99.14
 
0.3%
992
 
0.1%
98.92
 
0.1%
Distinct680
Distinct (%)48.5%
Missing100
Missing (%)6.7%
Memory size11.9 KiB
2025-10-23T11:14:50.347143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.4397719
Min length1

Characters and Unicode

Total characters4826
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique673 ?
Unique (%)48.0%

Sample

1st row100
2nd row66
3rd row120
4th row269
5th row73
ValueCountFrequency (%)
701718
51.2%
6132
 
0.1%
2202
 
0.1%
12
 
0.1%
2902
 
0.1%
2532
 
0.1%
4682
 
0.1%
651
 
0.1%
6541
 
0.1%
1001
 
0.1%
Other values (670)670
47.8%
2025-10-23T11:14:50.755755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1954
19.8%
7857
17.8%
0851
17.6%
+718
14.9%
2235
 
4.9%
4234
 
4.8%
6234
 
4.8%
5233
 
4.8%
3232
 
4.8%
8138
 
2.9%
Other values (2)140
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)4826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1954
19.8%
7857
17.8%
0851
17.6%
+718
14.9%
2235
 
4.9%
4234
 
4.8%
6234
 
4.8%
5233
 
4.8%
3232
 
4.8%
8138
 
2.9%
Other values (2)140
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1954
19.8%
7857
17.8%
0851
17.6%
+718
14.9%
2235
 
4.9%
4234
 
4.8%
6234
 
4.8%
5233
 
4.8%
3232
 
4.8%
8138
 
2.9%
Other values (2)140
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1954
19.8%
7857
17.8%
0851
17.6%
+718
14.9%
2235
 
4.9%
4234
 
4.8%
6234
 
4.8%
5233
 
4.8%
3232
 
4.8%
8138
 
2.9%
Other values (2)140
 
2.9%

International_Students_Score
Real number (ℝ)

High correlation  Missing 

Distinct521
Distinct (%)36.1%
Missing58
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean25.580346
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.9 KiB
2025-10-23T11:14:50.838332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.2
Q12.9
median9.6
Q338.3
95-th percentile97.58
Maximum100
Range99
Interquartile range (IQR)35.4

Descriptive statistics

Standard deviation31.098689
Coefficient of variation (CV)1.2157259
Kurtosis0.27178433
Mean25.580346
Median Absolute Deviation (MAD)8.1
Skewness1.2820318
Sum36963.6
Variance967.12846
MonotonicityNot monotonic
2025-10-23T11:14:50.931529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.139
 
2.6%
10033
 
2.2%
1.232
 
2.1%
1.628
 
1.9%
1.427
 
1.8%
1.524
 
1.6%
2.523
 
1.5%
1.721
 
1.4%
1.321
 
1.4%
1.917
 
1.1%
Other values (511)1180
78.5%
(Missing)58
 
3.9%
ValueCountFrequency (%)
114
 
0.9%
1.139
2.6%
1.232
2.1%
1.321
1.4%
1.427
1.8%
1.524
1.6%
1.628
1.9%
1.721
1.4%
1.814
 
0.9%
1.917
1.1%
ValueCountFrequency (%)
10033
2.2%
99.92
 
0.1%
99.86
 
0.4%
99.62
 
0.1%
99.53
 
0.2%
99.44
 
0.3%
99.32
 
0.1%
99.23
 
0.2%
99.12
 
0.1%
98.92
 
0.1%
Distinct690
Distinct (%)47.8%
Missing58
Missing (%)3.9%
Memory size11.9 KiB
2025-10-23T11:14:51.172707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.449827
Min length1

Characters and Unicode

Total characters4985
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique689 ?
Unique (%)47.7%

Sample

1st row143
2nd row44
3rd row73
4th row215
5th row98
ValueCountFrequency (%)
701756
52.3%
441
 
0.1%
731
 
0.1%
2151
 
0.1%
981
 
0.1%
2441
 
0.1%
631
 
0.1%
1291
 
0.1%
211
 
0.1%
1721
 
0.1%
Other values (680)680
47.1%
2025-10-23T11:14:51.484846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1990
19.9%
7896
18.0%
0883
17.7%
+756
15.2%
2239
 
4.8%
3238
 
4.8%
6236
 
4.7%
4236
 
4.7%
5235
 
4.7%
8139
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)4985
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1990
19.9%
7896
18.0%
0883
17.7%
+756
15.2%
2239
 
4.8%
3238
 
4.8%
6236
 
4.7%
4236
 
4.7%
5235
 
4.7%
8139
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4985
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1990
19.9%
7896
18.0%
0883
17.7%
+756
15.2%
2239
 
4.8%
3238
 
4.8%
6236
 
4.7%
4236
 
4.7%
5235
 
4.7%
8139
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4985
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1990
19.9%
7896
18.0%
0883
17.7%
+756
15.2%
2239
 
4.8%
3238
 
4.8%
6236
 
4.7%
4236
 
4.7%
5235
 
4.7%
8139
 
2.8%

International_Research_Network_Score
Real number (ℝ)

High correlation 

Distinct764
Distinct (%)50.9%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean50.129095
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.9 KiB
2025-10-23T11:14:51.582483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.605
Q122.725
median51.1
Q377.1
95-th percentile95.1
Maximum100
Range99
Interquartile range (IQR)54.375

Descriptive statistics

Standard deviation29.866588
Coefficient of variation (CV)0.59579348
Kurtosis-1.3192934
Mean50.129095
Median Absolute Deviation (MAD)26.95
Skewness-0.01028046
Sum75293.9
Variance892.01306
MonotonicityNot monotonic
2025-10-23T11:14:51.682808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.17
 
0.5%
93.16
 
0.4%
94.26
 
0.4%
24.56
 
0.4%
6.56
 
0.4%
55.56
 
0.4%
14.95
 
0.3%
615
 
0.3%
41.75
 
0.3%
68.65
 
0.3%
Other values (754)1445
96.1%
ValueCountFrequency (%)
15
0.3%
1.11
 
0.1%
1.22
 
0.1%
1.81
 
0.1%
21
 
0.1%
2.11
 
0.1%
2.21
 
0.1%
2.32
 
0.1%
2.42
 
0.1%
2.53
0.2%
ValueCountFrequency (%)
1001
 
0.1%
99.91
 
0.1%
99.82
0.1%
99.61
 
0.1%
99.54
0.3%
99.31
 
0.1%
99.22
0.1%
99.11
 
0.1%
98.81
 
0.1%
98.71
 
0.1%
Distinct701
Distinct (%)46.7%
Missing1
Missing (%)0.1%
Memory size11.9 KiB
2025-10-23T11:14:51.971746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.4620506
Min length1

Characters and Unicode

Total characters5200
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique700 ?
Unique (%)46.6%

Sample

1st row58
2nd row34
3rd row1
4th row5
5th row10
ValueCountFrequency (%)
701802
53.4%
101
 
0.1%
451
 
0.1%
641
 
0.1%
1461
 
0.1%
21
 
0.1%
5491
 
0.1%
1371
 
0.1%
391
 
0.1%
3541
 
0.1%
Other values (691)691
46.0%
2025-10-23T11:14:52.291728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11042
20.0%
7943
18.1%
0933
17.9%
+802
15.4%
5240
 
4.6%
6240
 
4.6%
2240
 
4.6%
4240
 
4.6%
3240
 
4.6%
9140
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)5200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11042
20.0%
7943
18.1%
0933
17.9%
+802
15.4%
5240
 
4.6%
6240
 
4.6%
2240
 
4.6%
4240
 
4.6%
3240
 
4.6%
9140
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11042
20.0%
7943
18.1%
0933
17.9%
+802
15.4%
5240
 
4.6%
6240
 
4.6%
2240
 
4.6%
4240
 
4.6%
3240
 
4.6%
9140
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11042
20.0%
7943
18.1%
0933
17.9%
+802
15.4%
5240
 
4.6%
6240
 
4.6%
2240
 
4.6%
4240
 
4.6%
3240
 
4.6%
9140
 
2.7%

Employment_Outcomes_Score
Real number (ℝ)

High correlation 

Distinct569
Distinct (%)37.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.825083
Minimum1.2
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.9 KiB
2025-10-23T11:14:52.365030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.8
Q14
median11.8
Q333.4
95-th percentile90.16
Maximum100
Range98.8
Interquartile range (IQR)29.4

Descriptive statistics

Standard deviation27.351315
Coefficient of variation (CV)1.148005
Kurtosis0.96598139
Mean23.825083
Median Absolute Deviation (MAD)9.1
Skewness1.4429491
Sum35809.1
Variance748.09445
MonotonicityNot monotonic
2025-10-23T11:14:52.627705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.931
 
2.1%
2.126
 
1.7%
3.925
 
1.7%
224
 
1.6%
1.821
 
1.4%
3.820
 
1.3%
3.720
 
1.3%
1.716
 
1.1%
2.216
 
1.1%
2.914
 
0.9%
Other values (559)1290
85.8%
ValueCountFrequency (%)
1.24
 
0.3%
1.313
0.9%
1.45
 
0.3%
1.512
 
0.8%
1.69
 
0.6%
1.716
1.1%
1.821
1.4%
1.931
2.1%
224
1.6%
2.126
1.7%
ValueCountFrequency (%)
1008
0.5%
99.94
0.3%
99.81
 
0.1%
99.71
 
0.1%
99.61
 
0.1%
99.11
 
0.1%
991
 
0.1%
98.91
 
0.1%
98.71
 
0.1%
98.61
 
0.1%
Distinct695
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
2025-10-23T11:14:52.879020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.4664005
Min length1

Characters and Unicode

Total characters5210
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique694 ?
Unique (%)46.2%

Sample

1st row8
2nd row61
3rd row3
4th row1
5th row5
ValueCountFrequency (%)
701809
53.8%
6981
 
0.1%
61
 
0.1%
6221
 
0.1%
81
 
0.1%
611
 
0.1%
731
 
0.1%
1571
 
0.1%
4071
 
0.1%
481
 
0.1%
Other values (685)685
45.6%
2025-10-23T11:14:53.169300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11044
20.0%
7949
18.2%
0939
18.0%
+809
15.5%
4240
 
4.6%
6239
 
4.6%
3239
 
4.6%
2238
 
4.6%
5237
 
4.5%
9139
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)5210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11044
20.0%
7949
18.2%
0939
18.0%
+809
15.5%
4240
 
4.6%
6239
 
4.6%
3239
 
4.6%
2238
 
4.6%
5237
 
4.5%
9139
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11044
20.0%
7949
18.2%
0939
18.0%
+809
15.5%
4240
 
4.6%
6239
 
4.6%
3239
 
4.6%
2238
 
4.6%
5237
 
4.5%
9139
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11044
20.0%
7949
18.2%
0939
18.0%
+809
15.5%
4240
 
4.6%
6239
 
4.6%
3239
 
4.6%
2238
 
4.6%
5237
 
4.5%
9139
 
2.7%

Sustainability_Score
Real number (ℝ)

High correlation  Missing 

Distinct395
Distinct (%)26.6%
Missing19
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean24.309299
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.9 KiB
2025-10-23T11:14:53.256653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.3
median6.75
Q339.5
95-th percentile92.285
Maximum100
Range99
Interquartile range (IQR)38.2

Descriptive statistics

Standard deviation31.074718
Coefficient of variation (CV)1.2783058
Kurtosis-0.020979949
Mean24.309299
Median Absolute Deviation (MAD)5.75
Skewness1.1918538
Sum36075
Variance965.63809
MonotonicityNot monotonic
2025-10-23T11:14:53.376934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1219
 
14.6%
1.183
 
5.5%
1.247
 
3.1%
1.332
 
2.1%
1.628
 
1.9%
1.723
 
1.5%
1.522
 
1.5%
1.421
 
1.4%
2.416
 
1.1%
3.114
 
0.9%
Other values (385)979
65.1%
(Missing)19
 
1.3%
ValueCountFrequency (%)
1219
14.6%
1.183
 
5.5%
1.247
 
3.1%
1.332
 
2.1%
1.421
 
1.4%
1.522
 
1.5%
1.628
 
1.9%
1.723
 
1.5%
1.89
 
0.6%
1.910
 
0.7%
ValueCountFrequency (%)
1002
0.1%
99.82
0.1%
99.72
0.1%
99.63
0.2%
99.31
 
0.1%
99.22
0.1%
99.12
0.1%
992
0.1%
98.91
 
0.1%
98.81
 
0.1%

Sustainability_Rank
Text

Missing 

Distinct340
Distinct (%)22.9%
Missing19
Missing (%)1.3%
Memory size11.9 KiB
2025-10-23T11:14:53.646746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.8268194
Min length1

Characters and Unicode

Total characters5679
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique149 ?
Unique (%)10.0%

Sample

1st row15=
2nd row6
3rd row126
4th row130
5th row127=
ValueCountFrequency (%)
701782
52.7%
3717
 
0.5%
5377
 
0.5%
5707
 
0.5%
6067
 
0.5%
6366
 
0.4%
6616
 
0.4%
4626
 
0.4%
6236
 
0.4%
3385
 
0.3%
Other values (330)645
43.5%
2025-10-23T11:14:53.973898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11028
18.1%
0927
16.3%
7912
16.1%
+782
13.8%
=553
9.7%
3269
 
4.7%
6253
 
4.5%
5238
 
4.2%
2236
 
4.2%
4216
 
3.8%
Other values (2)265
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)5679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11028
18.1%
0927
16.3%
7912
16.1%
+782
13.8%
=553
9.7%
3269
 
4.7%
6253
 
4.5%
5238
 
4.2%
2236
 
4.2%
4216
 
3.8%
Other values (2)265
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11028
18.1%
0927
16.3%
7912
16.1%
+782
13.8%
=553
9.7%
3269
 
4.7%
6253
 
4.5%
5238
 
4.2%
2236
 
4.2%
4216
 
3.8%
Other values (2)265
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11028
18.1%
0927
16.3%
7912
16.1%
+782
13.8%
=553
9.7%
3269
 
4.7%
6253
 
4.5%
5238
 
4.2%
2236
 
4.2%
4216
 
3.8%
Other values (2)265
 
4.7%

Overall_Score
Text

Missing 

Distinct363
Distinct (%)60.4%
Missing902
Missing (%)60.0%
Memory size11.9 KiB
2025-10-23T11:14:54.273013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.8036606
Min length1

Characters and Unicode

Total characters2286
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique217 ?
Unique (%)36.1%

Sample

1st row100
2nd row98.5
3rd row96.9
4th row96.8
5th row96.7
ValueCountFrequency (%)
23.67
 
1.2%
37.26
 
1.0%
24.56
 
1.0%
21.15
 
0.8%
22.45
 
0.8%
22.65
 
0.8%
22.85
 
0.8%
24.15
 
0.8%
20.85
 
0.8%
29.75
 
0.8%
Other values (353)547
91.0%
2025-10-23T11:14:54.646826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
.542
23.7%
2344
15.0%
3286
12.5%
4186
 
8.1%
5180
 
7.9%
1157
 
6.9%
6148
 
6.5%
7142
 
6.2%
8139
 
6.1%
9112
 
4.9%
Other values (2)50
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)2286
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.542
23.7%
2344
15.0%
3286
12.5%
4186
 
8.1%
5180
 
7.9%
1157
 
6.9%
6148
 
6.5%
7142
 
6.2%
8139
 
6.1%
9112
 
4.9%
Other values (2)50
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2286
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.542
23.7%
2344
15.0%
3286
12.5%
4186
 
8.1%
5180
 
7.9%
1157
 
6.9%
6148
 
6.5%
7142
 
6.2%
8139
 
6.1%
9112
 
4.9%
Other values (2)50
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2286
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.542
23.7%
2344
15.0%
3286
12.5%
4186
 
8.1%
5180
 
7.9%
1157
 
6.9%
6148
 
6.5%
7142
 
6.2%
8139
 
6.1%
9112
 
4.9%
Other values (2)50
 
2.2%

Interactions

2025-10-23T11:14:42.866732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:34.273380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:34.768165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:35.287685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:35.785253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:38.766989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:40.819213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:41.650646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:42.276011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:42.926622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:34.333969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:34.835365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:35.353647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:35.839861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:39.491165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:40.891729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:41.721375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:42.343131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:42.986978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:34.389706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:34.889764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:35.409091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:35.894618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:40.260401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:40.962746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:41.795245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:42.408497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:43.046807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:34.442995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:34.944795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:35.461477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:35.950744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:40.387527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:41.031579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:41.873849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:42.473511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:43.109686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:34.497232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:35.003403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:35.514733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:36.005515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:40.451673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:41.099535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:41.951594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:42.539189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:43.176971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:34.549849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:35.057275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:35.567979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:36.065340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:40.518975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:41.181578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:42.022547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:42.604486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:43.237263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:34.609397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:35.113153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:35.621430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:36.535838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:40.589764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:41.266745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:42.090049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:42.669980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:43.302144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:34.661762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:35.170902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:35.674282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:37.282021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:40.663697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:41.512399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:42.152921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:42.743758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:43.361637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:34.714730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:35.227999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:35.732439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:38.037277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:40.736702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:41.578231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:42.214917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T11:14:42.806586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-23T11:14:54.721195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Academic_Reputation_ScoreCitations_per_Faculty_ScoreEmployer_Reputation_ScoreEmployment_Outcomes_ScoreFOCUSFaculty_Student_ScoreInternational_Faculty_ScoreInternational_Research_Network_ScoreInternational_Students_ScoreRES.RegionSIZESTATUSSustainability_Score
Academic_Reputation_Score1.0000.4170.8180.6510.2160.3070.4400.6440.3730.1860.0730.1920.0920.725
Citations_per_Faculty_Score0.4171.0000.2950.3090.1080.0610.4150.6800.3780.3120.1340.0750.1390.640
Employer_Reputation_Score0.8180.2951.0000.6600.1370.2870.3940.4390.3430.1350.0390.1150.0750.600
Employment_Outcomes_Score0.6510.3090.6601.0000.1720.1940.3530.4770.3270.1040.0940.1570.0770.532
FOCUS0.2160.1080.1370.1721.0000.1070.0400.2570.0440.1430.1230.2570.0980.199
Faculty_Student_Score0.3070.0610.2870.1940.1071.0000.2330.1440.3060.1090.0740.1220.0670.232
International_Faculty_Score0.4400.4150.3940.3530.0400.2331.0000.4080.6860.1300.1860.0780.0850.472
International_Research_Network_Score0.6440.6800.4390.4770.2570.1440.4081.0000.4230.3720.1920.2710.2720.774
International_Students_Score0.3730.3780.3430.3270.0440.3060.6860.4231.0000.1440.1960.0800.0370.464
RES.0.1860.3120.1350.1040.1430.1090.1300.3720.1441.0000.1710.0460.1540.238
Region0.0730.1340.0390.0940.1230.0740.1860.1920.1960.1711.0000.1270.2610.164
SIZE0.1920.0750.1150.1570.2570.1220.0780.2710.0800.0460.1271.0000.2190.150
STATUS0.0920.1390.0750.0770.0980.0670.0850.2720.0370.1540.2610.2191.0000.104
Sustainability_Score0.7250.6400.6000.5320.1990.2320.4720.7740.4640.2380.1640.1500.1041.000

Missing values

2025-10-23T11:14:43.491878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-23T11:14:43.644514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-23T11:14:43.816681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

RANK_2025RANK_2024Institution_NameLocationRegionSIZEFOCUSRES.STATUSAcademic_Reputation_ScoreAcademic_Reputation_RankEmployer_Reputation_ScoreEmployer_Reputation_RankFaculty_Student_ScoreFaculty_Student_RankCitations_per_Faculty_ScoreCitations_per_Faculty_RankInternational_Faculty_ScoreInternational_Faculty_RankInternational_Students_ScoreInternational_Students_RankInternational_Research_Network_ScoreInternational_Research_Network_RankEmployment_Outcomes_ScoreEmployment_Outcomes_RankSustainability_ScoreSustainability_RankOverall_Score
011Massachusetts Institute of Technology (MIT)United StatesAmericasMCOVHB100.04100.02100.011100.0999.310086.814396.058100.0899.015=100
126Imperial College LondonUnited KingdomEuropeLFCVHA98.52299.51198.24593.954100.06699.64497.43493.46199.7698.5
233University of OxfordUnited KingdomEuropeLFCVHA100.02100.05100.0884.89398.112097.773100.01100.0385.012696.9
344Harvard UniversityUnited StatesAmericasLFCVHB100.01100.0196.353100.0174.126969.021599.65100.0184.413096.8
452University of CambridgeUnited KingdomEuropeLFCVHA100.03100.04100.01784.696100.07394.89899.310100.0584.8127=96.7
565Stanford UniversityUnited StatesAmericasLFCVHB100.05100.03100.01299.02770.328460.824496.845100.0281.2148=96.1
677ETH Zurich - Swiss Federal Institute of TechnologySwitzerlandEuropeLFOVHA98.81987.25765.919997.935100.02598.66395.76490.57398.81893.9
788National University of Singapore (NUS)SingaporeAsiaXLFCVHA99.51591.14868.818993.161100.04188.912991.6146100.0697.726=93.7
899UCLUnited KingdomEuropeXLFCVHA99.51498.31895.95672.214599.0106100.02199.9270.315774.8190=91.6
91015California Institute of Technology (Caltech)United StatesAmericasSCOVHB96.52995.333100.04100.05100.06579.817265.554931.040762.525390.9
RANK_2025RANK_2024Institution_NameLocationRegionSIZEFOCUSRES.STATUSAcademic_Reputation_ScoreAcademic_Reputation_RankEmployer_Reputation_ScoreEmployer_Reputation_RankFaculty_Student_ScoreFaculty_Student_RankCitations_per_Faculty_ScoreCitations_per_Faculty_RankInternational_Faculty_ScoreInternational_Faculty_RankInternational_Students_ScoreInternational_Students_RankInternational_Research_Network_ScoreInternational_Research_Network_RankEmployment_Outcomes_ScoreEmployment_Outcomes_RankSustainability_ScoreSustainability_RankOverall_Score
14931401+1201-1400Universiti Sains Islam MalaysiaMalaysiaAsiaMCOHIA6.6601+2.5601+8.8701+1.9701+8.4701+6.1701+12.5701+3.3701+1.0701+NaN
14941401+1201-1400University of Central OklahomaUnited StatesAmericasLCOMDA5.6601+2.9601+6.5701+1.5701+15.96549.9701+5.2701+2.9701+1.0701+NaN
14951401+1401+University of CraiovaRomaniaEuropeLCOHIA4.5601+2.2601+3.2701+2.1701+NaNNaNNaNNaN21.8701+13.4701+1.0701+NaN
14961401+1401+University of LampungIndonesiaAsiaLFCMDNaN4.7601+2.3601+5.1701+1.3701+1.2701+1.0701+3.4701+2.2701+1.0701+NaN
14971401+1401+University of MataramIndonesiaAsiaXLFOMDA6.4601+1.7601+3.8701+1.1701+1.5701+1.0701+3.7701+2.2701+1.0701+NaN
14981401+1201-1400University of Montana MissoulaUnited StatesAmericasMCOHIA3.0601+2.2601+10.6701+6.1701+1.3701+1.9701+6.5701+3.1701+1.0701+NaN
14991401+1401+University of OradeaRomaniaEuropeLFCMDA5.6601+2.2601+4.0701+1.9701+1.5701+5.2701+34.5701+6.2701+2.3701+NaN
15001401+1201-1400University of San CarlosPhilippinesAsiaMCOMDC7.2601+9.4601+3.3701+1.8701+2.1701+2.1701+6.4701+9.6701+1.0701+NaN
15011401+1401+University Politehnica of Timisoara, UPTRomaniaEuropeLFOVHA4.1601+4.2601+7.2701+3.9701+1.4701+2.5701+18.6701+3.9701+1.1701+NaN
15021401+1401+Western Washington UniversityUnited StatesAmericasLCOHINaN2.6601+2.6601+7.3701+3.5701+9.7701+1.6701+12.4701+1.5701+1.1701+NaN